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Music similarity has been widely studied through melodic
and harmonic matching, clustering, and using various metrics for
measuring distance. Such analyses offer the musicologist a view of
the ‘sameness’ of parts of a score. However, similarity alone does
not necessarily allow exploitation of that sameness in reasoning about
the music. In this paper, we present work in progress to investigate
rhythm similarity at various scales, beginning at the smallest (single
measures or groups of measures). We use normalised compression
distance and variations thereof to derive similarity-based dependencies
between parts of the music. Establishing such dependencies may allow
software engineering dependence analysis techniques to be applied to
music to, e.g. remove from focus aspects not relevant to a particular
enquiry (‘slicing’), determine the sensitivity of later parts of the music
on former parts (‘impact analysis’), and to find motivic processes and
developments within the musical form.
The analysis will thus draw on software engineering techniques, information
theory, and data compression. Our results thus far show
that text-based compressors introduce significant non-linear artefacts
at small scales making similarity identification based on compressed
lengths difficult. Future work will involve progressively larger scale
music to determine the sensitivity of the results to the size of music
being analysed in order to guide musicologists wanting to adopt similar
approaches. We expect to find that at larger scales, the artefacts in
text compression become less significant and identifying the threshold
at which this happens is thus important. We discuss tree compression
as having the potential to capture musically-important relationships
lost by text compression and believe that this approach would be more
successful at small scales.